OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing
Venue: arXiv cs.SE 2025
Year: 2025

The emergence of foundational models and generative artificial intelligence (GenAI) is poised to transform productivity in scientific computing, especially in code development, refactoring, and translating from one programming language to another. However, because the output of GenAI cannot be guaranteed to be correct, manual intervention remains necessary. Some of this intervention can be automated through task-specific tools, alongside additional methodologies for correctness verification and effective prompt development. We explored the application of GenAI in assisting with code translation, language interoperability, and codebase inspection within a legacy Fortran codebase used to simulate particle interactions at the Large Hadron Collider (LHC). In the process, we developed a tool, CodeScribe, which combines prompt engineering with user supervision to establish an efficient process for code conversion. In this paper, we demonstrate how CodeScribe assists in converting Fortran code to C++, generating Fortran-C APIs for integrating legacy systems with modern C++ libraries, and providing developer support for code organization and algorithm implementation. We also address the challenges of AI-driven code translation and highlight its benefits for enhancing productivity in scientific computing workflows.
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Venue: arXiv cs.CL 2025
Year: 2025

Large language models have shown strong reasoning capabilities through chain-structured methods such as Chain-of-Thought. Recent studies optimize thought structures by generating parallel or tree-like structures, switching between long and short reasoning modes, or aligning reasoning steps with task performance. However, these approaches mainly rely on previously generated logical directions of the chains, which ignore the unexplored regions of the solution space. Such a phenomenon is defined as blind spots, which limit the diversity and effectiveness of the reasoning process. To this end, we propose the ``Thought Space Explorer'' (TSE), a framework for navigating and expanding thought structures to overcome blind spots in LLM reasoning. Our TSE first identifies key nodes with high impact, then generates new nodes by integrating information from multiple chains. Finally, it extends new branches through connection strategies. We conduct a series of experiments on math and QA benchmarks. Compared with existing baseline methods, TSE improves the accuracy of both the final answer and intermediate reasoning steps, while maintaining a better effectiveness-efficiency trade-off for practical deployment.
Constraint Back-translation Improves Complex Instruction Following of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs. However, even advanced LLMs cannot follow complex instructions well, thus limiting the quality of generated data. In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. Specifically, we take the high-quality instruction-response pairs in existing datasets and only adopt advanced LLMs to add complex constraints already met by the responses to the instructions, which naturally reduces costs and data noise. In the experiments, we adopt Llama3-70B-Instruct to back-translate constraints and create a high-quality complex instruction-response dataset, named CRAB. We present that post-training on CRAB improves multiple backbone LLMs' complex instruction-following ability, evaluated on extensive instruction-following benchmarks. We further find that constraint back-translation also serves as a useful auxiliary training objective in post-training. Our code, data, and models will be released to facilitate future research.
Length-Induced Embedding Collapse in PLM-based Models
Venue: arXiv cs.CL 2025
Year: 2025

Text embeddings from PLM-based models enable a wide range of applications, yet their performance often degrades on longer texts. In this paper, we introduce a phenomenon we call Length Collapse, where embeddings of longer texts tend to cluster together. This clustering results in a distributional inconsistency between the embeddings of short and long texts. We further investigate how these differences contribute to the performance decline observed with longer texts across various downstream tasks. Through a rigorous theoretical analysis of the self-attention mechanism, which acts as a low-pass filter in PLM-based models, we demonstrate that as text length increases, the strength of low-pass filtering intensifies, causing embeddings to retain more low-frequency components. As a result, input token features become more similar, leading to clustering and ultimately the collapse of embeddings for longer texts. To address this issue, we propose a simple method, TempScale, which mitigates the Length Collapse phenomenon. By narrowing the gap in low-pass filtering rates between long and short texts, TempScale ensures more consistent embeddings across different text lengths. This approach leads to performance improvements of 0.94% on MTEB and 1.10% on LongEmbed, which focuses specifically on long-context retrieval, providing strong evidence for the validity of our analysis. The source code is available at https://github.com/Yuqi-Zhou/Length_Collapse.
Personalization of Large Language Models: A Survey
Venue: arXiv cs.CL 2025
Year: 2025

Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. In this work, we bridge the gap between these two separate main directions for the first time by introducing a taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide a formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomies for the granularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim to provide a clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.
WikiNER-fr-gold: A Gold-Standard NER Corpus
Venue: arXiv cs.CL 2025
Year: 2025

We address in this article the the quality of the WikiNER corpus, a multilingual Named Entity Recognition corpus, and provide a consolidated version of it. The annotation of WikiNER was produced in a semi-supervised manner i.e. no manual verification has been carried out a posteriori. Such corpus is called silver-standard. In this paper we propose WikiNER-fr-gold which is a revised version of the French proportion of WikiNER. Our corpus consists of randomly sampled 20% of the original French sub-corpus (26,818 sentences with 700k tokens). We start by summarizing the entity types included in each category in order to define an annotation guideline, and then we proceed to revise the corpus. Finally we present an analysis of errors and inconsistency observed in the WikiNER-fr corpus, and we discuss potential future work directions.
Masking Gaussian Elimination at Arbitrary Order, with Application to Multivariate- and Code-Based PQC
Venue: arXiv cs.CR 2025
Year: 2025

Digital signature schemes based on multivariate- and code-based hard problems are promising alternatives for lattice-based signature schemes, due to their small signature size. Gaussian Elimination (GE) is a critical operation in the signing procedure of these schemes. In this paper, we provide a masking scheme for GE with back substitution to defend against first- and higher-order attacks. To the best of our knowledge, this work is the first to analyze and propose masking techniques for multivariate- or code-based DS algorithms. We propose a masked algorithm for transforming a system of linear equations into row-echelon form. This is realized by introducing techniques for efficiently making leading (pivot) elements one while avoiding costly conversions between Boolean and multiplicative masking at all orders. We also propose a technique for efficient masked back substitution, which eventually enables a secure unmasking of the public output. All novel gadgets are proven secure in the $t$-probing model. Additionally, we evaluate the overhead of our countermeasure for several post-quantum candidates and their different security levels at first-, second-, and third-order, including UOV, MAYO, SNOVA, QR-UOV, and MQ-Sign. Notably, the operational cost of first-, second-, and third-order masked GE is 2.3$\times$ higher, and the randomness cost is 1.2$\times$ higher in MAYO compared to UOV for security levels III and V. In contrast, these costs are similar in UOV and MAYO for one version of level I. We also show detailed performance results for masked GE implementations for all three security versions of UOV on the Arm Cortex-M4 and compare them with unmasked results. Our masked implementation targeting UOV parameters has an overhead of factor 15.1$\times$, 15.2$\times$, and 15.4$\times$ compared to the unprotected implementation for NIST security level I, III, and V.
Lagrangian neural networks for nonholonomic mechanics
Venue: arXiv cs.LG 2025
Year: 2025

Lagrangian Neural Networks (LNNs) are a powerful tool for addressing physical systems, particularly those governed by conservation laws. LNNs can parametrize the Lagrangian of a system to predict trajectories with nearly conserved energy. These techniques have proven effective in unconstrained systems as well as those with holonomic constraints. In this work, we adapt LNN techniques to mechanical systems with nonholonomic constraints. We test our approach on some well-known examples with nonholonomic constraints, showing that incorporating these restrictions into the neural network's learning improves not only trajectory estimation accuracy but also ensures adherence to constraints and exhibits better energy behavior compared to the unconstrained counterpart.
Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking
Venue: arXiv cs.CL 2025
Year: 2025

Zero-shot domain adaptation for dialogue state tracking (DST) remains a challenging problem in task-oriented dialogue (TOD) systems, where models must generalize to target domains unseen at training time. Current large language model approaches for zero-shot domain adaptation rely on prompting to introduce knowledge pertaining to the target domains. However, their efficacy strongly depends on prompt engineering, as well as the zero-shot ability of the underlying language model. In this work, we devise a novel data augmentation approach, Schema Augmentation, that improves the zero-shot domain adaptation of language models through fine-tuning. Schema Augmentation is a simple but effective technique that enhances generalization by introducing variations of slot names within the schema provided in the prompt. Experiments on MultiWOZ and SpokenWOZ showed that the proposed approach resulted in a substantial improvement over the baseline, in some experiments achieving over a twofold accuracy gain over unseen domains while maintaining equal or superior performance over all domains.
Aquatic-GS: A Hybrid 3D Representation for Underwater Scenes
Venue: arXiv cs.CV 2025
Year: 2025

Representing underwater 3D scenes is a valuable yet complex task, as attenuation and scattering effects during underwater imaging significantly couple the information of the objects and the water. This coupling presents a significant challenge for existing methods in effectively representing both the objects and the water medium simultaneously. To address this challenge, we propose Aquatic-GS, a hybrid 3D representation approach for underwater scenes that effectively represents both the objects and the water medium. Specifically, we construct a Neural Water Field (NWF) to implicitly model the water parameters, while extending the latest 3D Gaussian Splatting (3DGS) to model the objects explicitly. Both components are integrated through a physics-based underwater image formation model to represent complex underwater scenes. Moreover, to construct more precise scene geometry and details, we design a Depth-Guided Optimization (DGO) mechanism that uses a pseudo-depth map as auxiliary guidance. After optimization, Aquatic-GS enables the rendering of novel underwater viewpoints and supports restoring the true appearance of underwater scenes, as if the water medium were absent. Extensive experiments on both simulated and real-world datasets demonstrate that Aquatic-GS surpasses state-of-the-art underwater 3D representation methods, achieving better rendering quality and real-time rendering performance with a 410x increase in speed. Furthermore, regarding underwater image restoration, Aquatic-GS outperforms representative dewatering methods in color correction, detail recovery, and stability. Our models, code, and datasets can be accessed at https://aquaticgs.github.io.
ResiDual Transformer Alignment with Spectral Decomposition
Venue: arXiv cs.CV 2025
Year: 2025

When examined through the lens of their residual streams, a puzzling property emerges in transformer networks: residual contributions (e.g., attention heads) sometimes specialize in specific tasks or input attributes. In this paper, we analyze this phenomenon in vision transformers, focusing on the spectral geometry of residuals, and explore its implications for modality alignment in vision-language models. First, we link it to the intrinsically low-dimensional structure of visual head representations, zooming into their principal components and showing that they encode specialized roles across a wide variety of input data distributions. Then, we analyze the effect of head specialization in multimodal models, focusing on how improved alignment between text and specialized heads impacts zero-shot classification performance. This specialization-performance link consistently holds across diverse pre-training data, network sizes, and objectives, demonstrating a powerful new mechanism for boosting zero-shot classification through targeted alignment. Ultimately, we translate these insights into actionable terms by introducing ResiDual, a technique for spectral alignment of the residual stream. Much like panning for gold, it lets the noise from irrelevant unit principal components (i.e., attributes) wash away to amplify task-relevant ones. Remarkably, this dual perspective on modality alignment yields fine-tuning level performance on different data distributions while modelling an extremely interpretable and parameter-efficient transformation, as we extensively show on 70 pre-trained network-dataset combinations (7 models, 10 datasets).
Incentive-based Platoon Formation: Optimizing the Personal Benefit for Drivers
Venue: arXiv cs.MA 2025
Year: 2025

Platooning or cooperative adaptive cruise control (CACC) has been investigated for decades, but debate about its lasting impact is still ongoing. While the benefits of platooning and the formation of platoons are well understood for trucks, they are less clear for passenger cars, which have a higher heterogeneity in trips and drivers' preferences. Most importantly, it remains unclear how to form platoons of passenger cars in order to optimize the personal benefit for the individual driver. To this end, in this paper, we propose a novel platoon formation algorithm that optimizes the personal benefit for drivers of individual passenger cars. For computing vehicle-to-platoon assignments, the algorithm utilizes a new metric that we propose to evaluate the personal benefits of various driving systems, including platooning. By combining fuel and travel time costs into a single monetary value, drivers can estimate overall trip costs according to a personal monetary value for time spent. This provides an intuitive way for drivers to understand and compare the benefits of driving systems like human driving, adaptive cruise control (ACC), and, of course, platooning. Unlike previous similarity-based methods, our proposed algorithm forms platoons only when beneficial for the driver, rather than solely for platooning. We demonstrate the new metric for the total trip cost in a numerical analysis and explain its interpretation. Results of a large-scale simulation study demonstrate that our proposed platoon formation algorithm outperforms normal ACC as well as previous similarity-based platooning approaches by balancing fuel savings and travel time, independent of traffic and drivers' time cost.
Diversity in Network-Friendly Recommendations
Venue: arXiv cs.PF 2025
Year: 2025

In recent years, the Internet has been dominated by content-rich platforms, employing recommendation systems to provide users with more appealing content (e.g., videos in YouTube, movies in Netflix). While traditional content recommendations are oblivious to network conditions, the paradigm of Network-Friendly Recommendations (NFR) has recently emerged, favoring content that improves network performance (e.g. cached near the user), while still being appealing to the user. However, NFR algorithms sometimes achieve their goal by shrinking the pool of content recommended to users. The undesirable side-effect is reduced content diversity, a phenomenon known as ``content/filter bubble''. This reduced diversity is problematic for both users, who are prevented from exploring a broader range of content, and content creators (e.g. YouTubers) whose content may be recommended less frequently, leading to perceived unfairness. In this paper, we first investigate - using real data and state-of-the-art NFR schemes - the extent of this phenomenon. We then formulate a ``Diverse-NFR'' optimization problem (i.e., network-friendly recommendations with - sufficient - content diversity), and through a series of transformation steps, we manage to reduce it to a linear program that can be solved fast and optimally. Our findings show that Diverse-NFR can achieve high network gains (comparable to non-diverse NFR) while maintaining diversity constraints. To our best knowledge, this is the first work that incorporates diversity issues into network-friendly recommendation algorithms.
Phase Diagram of Vision Large Language Models Inference: A Perspective from Interaction across Image and Instruction
Venue: arXiv cs.CL 2025
Year: 2025

Vision Large Language Models (VLLMs) usually take input as a concatenation of image token embeddings and text token embeddings and conduct causal modeling. However, their internal behaviors remain underexplored, raising the question of interaction among two types of tokens. To investigate such multimodal interaction during model inference, in this paper, we measure the contextualization among the hidden state vectors of tokens from different modalities. Our experiments uncover a four-phase inference dynamics of VLLMs against the depth of Transformer-based LMs, including (I) Alignment: In very early layers, contextualization emerges between modalities, suggesting a feature space alignment. (II) Intra-modal Encoding: In early layers, intra-modal contextualization is enhanced while inter-modal interaction is suppressed, suggesting a local encoding within modalities. (III) Inter-modal Encoding: In later layers, contextualization across modalities is enhanced, suggesting a deeper fusion across modalities. (IV) Output Preparation: In very late layers, contextualization is reduced globally, and hidden states are aligned towards the unembedding space.
Transparent Tagging for Strategic Social Nudges on User-Generated Misinformation
Venue: arXiv cs.GT 2025
Year: 2025

Social network platforms (SNP) rely heavily on user-generated content to attract users, yet they have limited control over content provision, which leads to misinformation. As countermeasures, SNPs have implemented policies to notify users by tagging the content and influencing users' responses to the tagged content. The population-level response creates a social nudge to the content provider that encourages it to supply more authentic content. Yet, when designing tags to leverage social nudges, SNP must be cautious about misdetection, which impairs its ability to create social nudges. We establish a Bayesian persuaded branching process to study SNP's tagging policy design under misdetection. Misinformation circulation is modeled by a multi-type branching process, where users are persuaded through tags to give positive/negative comments that influence misinformation spread. When translated into posterior belief space, the SNP's problem is reduced to an equality-constrained optimization, the optimal condition of which is given by the Lagrangian characterization. The key finding is that SNP's optimal policy is transparent tagging, albeit misdetection, which nudges the provider not to generate misinformation.
Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation
Venue: arXiv cs.CL 2025
Year: 2025

In the field of large language model (LLM)-based proof generation, despite extensive training on large datasets such as ArXiv, LLMs still exhibit only modest performance on proving tasks of moderate difficulty. We believe that this is partly due to the widespread presence of suboptimal ordering within the data for each proof used in training. For example, published proofs often follow a purely logical order, where each step logically proceeds from the previous steps based on the deductive rules. This order is designed to facilitate the verification of the proof's soundness, rather than to help people and models learn the discovery process of the proof. In proof generation, we argue that the optimal order for one training data sample occurs when the relevant intermediate supervision for a particular proof step in the proof is always positioned to the left of that proof step. We call such order the intuitively sequential order. We validate our claims using two tasks: intuitionistic propositional logic theorem-proving and digit multiplication. Our experiments verify the order effect and provide support for our explanations. We demonstrate that training is most effective when the proof is in the intuitively sequential order. Moreover, the order effect and the performance gap between models trained on different data orders can be substantial -- with an 11 percent improvement in proof success rate observed in the propositional logic theorem-proving task, between models trained on the optimal order compared to the worst order. Lastly, we define a common type of order issue in advanced math proofs and find that 17.3 percent of theorems with nontrivial proofs in the first two chapters of a widely used graduate-level mathematics textbook suffer from this issue. A detailed list of those proofs is provided in the appendix.